The feasibility of measuring calprotectin from a throat swab as a marker of infections caused by group A streptococcus: a case–control feasibility study
Bibliographic record
Abstract
BACKGROUND: Most people with sore throat do not benefit from antibiotic treatment, but nearly three-quarters of those presenting in primary care are prescribed antibiotics. A test that is predictive of bacterial infection could help guide antibiotic prescribing. Calprotectin is a biomarker of neutrophilic inflammation, and may be a useful marker of bacterial throat infections. AIM: To assess the feasibility of measuring calprotectin from throat swabs, and assess whether individuals with sore throats likely to be caused by streptococcal infections have apparently higher throat calprotectin levels than other individuals with sore throat and healthy volunteers. DESIGN & SETTING: A proof of concept case-control study was undertaken, which compared primary care patients with sore throats and healthy volunteers. METHOD: Baseline characteristics and throat swabs were collected from 30 primary care patients with suspected streptococcal sore throat, and throat swabs were taken from 10 volunteers without sore throat. Calprotectin level determination and rapid antigen streptococcal testing were conducted on the throat swab eluents. Calprotectin levels in the following groups were compared: volunteers without a sore throat; all patients with a sore throat; patients with a sore throat testing either negative or positive for streptococcal antigen; and those with lower and higher scores on clinical prediction rules for streptococcal sore throat. RESULTS: Calprotectin was detected in all throat swab samples. Mean calprotectin levels were numerically higher in patients with sore throat compared with healthy volunteers, and sore throat patients who had group A streptococci antigen detected compared with those who did not. CONCLUSION: Calprotectin can be measured from throat swab samples and levels are consistent with the hypothesis that streptococcal infection leads to higher throat calprotectin levels. This hypothesis will be tested in a larger study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".